Identification of the autonomic nervous system (ANS) balance within the regulation of the heart rhythm (HR) is based on the fact that the duration of every single heartbeat is determined by the effect of the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) on the heart sinus node. Due to this effect short-term phase HR acceleration and deceleration occur. However, it has been established that circadian rhythms influence fluctuations in the change of cardiovascular system day and night functions and metabolic functions. It had recently been proven that central and peripheral circadian pacemakers exercise partial direct control of the cardiovascular system and metabolic processes. This suggests that HR observation may help to safely exclude various parameters from the circadian oscillations. These parameters are of high importance for the improvement of an individual's sleep, physical and mental status and health. Their identification will help to adapt an individual to night shifts, avoid sleep disorders, optimize professional activities, increase performance, and improve sporting achievements and quality of life.
Steady slow HR is related to the tonic PNS predominance on HR regulation and fast HR is related to the tonic SNS predominance. HR is involved in the energy and blood supply to the whole body and it is readily affected by respiration, physical and nerve stimulants. Short-term HR fluctuations can be easily observed due to these stimulants. Well-known structures, such as an ANS sinus node and receptor system, participate in their development.
Beside the aforementioned regularities, HR also contains stochastic elements, such as: nerve impulsing; mediator release in the synapse or; action potential formation in the node. Processes of synthesis of various active substances, e.g., ion channel activation and inactivation and matter diffusion, could be observed at this level. This is the diffusion part of the HR process.
Heart rhythm is directly related to body energy requirements. Heart physiology shows that two main states are observed: activity and rest that are distinctly manifesting as changes of day and night rhythm. Body energy requirements are maximum at one instance and minimum at another. This is caused by the level of energy consumption which is equivalently reflected by HR. HR is the indicator of the energy consumption in the body and allows describing and easily defining the consumption ratio between two states over a period of observation. Basic bimodal distribution of HR with some deviations is usually observed; these deviations are caused by unsteadiness of external and internal body activities.
HR regulation by SNS and PNS is fairly complex. However, in the majority of cases, it is exercised through ANS, except for humoral responses. SNS effect on HR is quite slow and is related to the increase of body's energy demands due to the increase of heart rate, blood pressure and/or cardiac output. The PNS component responds much faster compared with the sympathetic component and reduces heart rate, blood pressure and/or cardiac output. At rest and during sleep proper balance between SNS and PNS is achieved very quickly. This sympatho-vagal balance is extremely important both for healthy and ill body reactions to various stimuli.
Using HR variability (HRV) methods ANS balance of short HR segments is defined as the ratio of two components of the spectrum of intervals between successive heartbeats (RR intervals): low frequencies (LF) and high frequencies (HF) ratio. One of the conditions of the analysis is stationary and linear origin of the RR series. Such conditions are possible when a subject itself is in a steady state. Otherwise, results are unsteady and hardly reproducible especially during long-term heart rate registration. The high frequency component of the heart rhythm frequently remains constant enough, whereas investigations of the low frequency component currently gives rise to doubts and criticism. Short-term (up to 5 minutes) circadian RR interval series are usually used to draw such conclusion. These calculations may be more precise when series duration is increased, and when an observation period covers a 24-hour period, the impact of the additional frequency component and unsteadiness occurs. Currently, circadian rhythm is being analyzed using HRV methods. It is clear that within 24 hours, heart rate changes not only due to the short-term external and internal stimuli but also due to the circadian biorhythm characterized by much higher dispersion. Obtained circadian RR series are composed of the sum of circadian biorhythm, various unsteady fluctuations and chaotic part.
A patient's metabolism circadian rhythm may be established by the registration of whole body temperature in various body sites (US 20110144528 (A1), U.S. Pat. No. 5,304,212 (A)) and by the measurement of the illumination of the environment (U.S. Pat. No. 5,176,133 (A), US2008065177 (A1), US 2005001512 (A1), WO 2013184627 (A1)). An analysis system is known when circadian rhythm is calculated from patient's blood carbon dioxide (CO2) parameters using an implantable medical device (US 2004138716 (A1)). Recent studies show that body cells have their own circadian clocks. Gene measurements have updated these studies (US 2011193779 (A1)).
A more precise system for the estimation of circadian rhythm combining together fitting and regression analysis for a series of RR intervals (intervals between two cardiac contractions) and electrocardiographic interval (QT) series derived from the ECG (electrocardiogram) series obtained by Holter monitoring is presented in the application for the patent US 20100292597 (A1). This method is based on the cosine function application for the whole series that is used for the identification of circadian rhythm within data obtained by registering body temperature. These methods use an assumption that circadian period presents clear cosine-wave shape. However, an ordinary person works, eats, sleeps, moves, has various diseases, and changes if his heart rhythm depends on the changes of metabolism, and therefore shape of circadian period is not always present as regular cosinusoid. This causes more dynamic changes and intrinsic circadian periods of the HR. Therefore, a more precise diagnostic technique is needed able to derive a true shape of circadian HR period and its parameters from the RR interval series.
Identification of the circadian rhythm using simulation principles when the factor causing rhythm changes is known (EP2447866 (A1)) is also used. In this model, an input signal could be RR series or other signal related to the circadian dynamics of the heart rhythm, such as acti-graph presented in this patent. Autoregressive-moving-average model (ARMAX) is applied to the combination of first signal and second input signal. Output signal, namely, circadian period, is fully dependent on periodicity of the input signal and may strongly influence the result, circadian rhythm signal. In order to represent (sharpen) circadian rhythm, a cosinor regression model is additionally used to obtain aforementioned cosine-wave shape. The method described requires registration of the additional signal, and results are linearly dependent on its representation of circadian rhythm.
Obtained series of circadian RR intervals are composed of individual circadian biorhythm and the sum of various unsteady changes. Prior inventions are not ultimately related to the principles of the regulation of heart rhythm, specifically with the peculiarities of autonomic nervous system and other systems. It has long been known that slow HR is related to the predominant effect of tonic PNS on the HR regulation and quick HR is related to the predominance of tonic SNS. Therefore, study findings do not contain results of the analysis in terms of further changes of circadian rhythm.
Published Patent Application US 20040193066 (A1) presents a heart rhythm managing system demonstrating the patient's state; it is based on the balance between SNS and PNS components and uses RR interval signal filtration in two bandwidths not limiting strictly filter characteristics, and therefore further processing results (dispersion is calculated) may differ from the data described in other known sources.
Japanese application JP2004283523 (A) describes the system for the analysis of the balance of autonomic nervous system comprising a unit for collecting circadian RR intervals, tools for artifacts and arrhythmias removal from the RR interval series, circadian period identifier and RR data series analyzer determining balance of the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS).
This system for the analysis of the autonomic nervous system balance for the first time allowed determining circadian period from heart rhythm. Detection of the circadian balance of the SNS and PNS elements of the autonomic nervous system is based on well-known methods of heart rhythm variability that are used for a long time (Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use, Circulation, 93, (1996), pp. 1043-1065; Heart Rate Variability: Standards of Measurement, Physiological Interpretation, and Clinical Use, European Heart Journal, 17, published by the American Heart Association, Inc.; European Society of Cardiology, (1996), pp. 354-381). The simplest method is employed by splitting circadian series of the heart rhythm by frequency distribution using fast Fourier transform (FFT) method. Data are collected using Holter monitors, and RR intervals are selected and arrhythmias and artifacts are excluded. Forward and backward Fourier transform is applied to circadian heart rhythm data and a variation curve is obtained. Accuracy of the analysis depends on series length and duration of the period selected from the bandwidth in the analyzed series. Basically, accuracy of the Fourier transform depends on the length of the series and number of periods of interest in this analyzed series. Consequently, only one period is present in the 24-hours series, and therefore the period discovered contains large errors and is inaccurate.
The described analysis system focus on the detection of two intersections points on the transition curve obtained after inverse Fourier transform showing on the curve, according to the inventors, changes of the vegetative nervous system 24-hours rhythm. Moreover, it is unclear what should be done with 48-hours data containing 4 points. It has not been considered that true ANS circadian rhythm due to internal hormonal fluctuations and temperature dynamics is governed by circadian metabolic rhythm possessing sinusoid shape and two intersection points. Heart rhythm follows development of the body metabolic processes; however, it is influenced also by other internal (hormones, blood pressure) and external (physical and nerve) factors. Therefore, the period of circadian heart rhythm reflects not only the main circadian metabolic rhythm of the body but also full ANS impact on the heart rhythm and exclusively may have several shorter periods and intersection points. Only circadian heart rhythm period comprising components of ANS and other components (known and unknown) could be evaluated. Obtained circadian period of heart rhythm approximated using a cosinor method to the cosine-wave shape could contain sizable errors as suggested in the aforementioned source US 201003292597 (A1).